The fate of "small" open source

I came across this once before in the form of a react hooks library that had no dependency to install. It was just a website and when you found the hook you wanted you were meant to paste it into your project.

  Given that some 80% of developers are now using AI in their regular work, blob-util is almost certainly the kind of thing that most developers would just happily have an LLM generate for them. Sure, you could use blob-util, but then you’d be taking on an extra dependency, with unknown performance, maintenance, and supply-chain risks.

Is "reversing a binary tree" actually a thing, or is this a cute kind of "rocket surgery" phrase intentionally mixing reversing a linked list and searching a binary tree?

I’ve been trying to encourage forking my libraries, or have people just copy them into their codebase and adapt them, e.g. https://github.com/mastrojs/mastro/ (especially the “extension” libs.) But it’s an uphill battle against the culture of convenience over understanding.

That’s not automatically a problem, however. The problem is that even if you do come up with a really cool idea that LLM is not capable of autocompleting, and you license it under a copyleft license (to ensure the project survives and volunteer contributor’s work is not adopted and extinguished by some commercial product), it will get incorporated into its dataset regardless of the licensing, and thereafter the LLM will be capable of spitting it out and its large corporate operator will be able to monetise your code (allowing anyone with money wishing to build a commercial product based on it).

Why would randomized code be more robust? Also, how is searching/reading the docs slower than checking the correctness of a randomized function?

It doesn't have to be this way- coupled with verification (to mitigate hallucination), llms can help so much with personal education.

Otherwise, writing it myself is much better. It's more customizable and often much smaller in size. This is because the library has to generalize, and it comes with bloats.

Using AI to help write is great because I should understand that anyway whether AI writes it or not or whether it's in an external library.

One example recently is that I built a virtual list myself. The code is much smaller and simpler compared to other popular libraries. But of course it's not as generalizable.

Node.js is very good for IO and it has decent performance even for CPU-intensive work considering it's a dynamic language, but it would sure be nice to have a rich core library like Ruby or Clojure has.

The fact that ClojureScript can do it proves that it's doable even for front-end javascript (using advanced optimisations).